AI and scientific computing applications are great examples of distributed computing problems. The problems are too large and the computations too intensive to run on a single machine. These computations are broken down into parallel tasks that are distributed across thousands of compute engines, such as CPUs and GPUs. To achieve scalable performance, the system relies on dividing workloads…
]]>In today’s rapidly evolving technological landscape, staying ahead of the curve is not just a goal—it’s a necessity. The surge of innovations, particularly in AI, is driving dramatic changes across the technology stack. One area witnessing profound transformation is Ethernet networking, a cornerstone of digital communication that has been foundational to enterprise and data center…
]]>Supercomputers are used to model and simulate the most complex processes in scientific computing, often for insight into new discoveries that otherwise would be impractical or impossible to demonstrate physically. The NVIDIA BlueField data processing unit (DPU) is transforming high-performance computing (HPC) resources into more efficient systems, while accelerating problem solving across a…
]]>Supercomputers are significant investments. However they are extremely valuable tools for researchers and scientists. To effectively and securely share the computational might of these data centers, NVIDIA introduced the Cloud-Native Supercomputing architecture. It combines bare metal performance, multitenancy, and performance isolation for supercomputing. Magnum IO, the I/
]]>Today’s data centers host many users and a wide variety of applications. They have even become the key element of competitive advantage for research, technology, and global industries. With the increased complexity of scientific computing, data center operational costs also continue to rise. In addition to the operational disruption of security threats, keeping a data center intact and running…
]]>